The connected world, also referred to as the internet of things, is growing quickly. Analysts have estimated that along with the continued growth of humans using the Internet, the number of connected devices and systems will rise from five billion to one trillion in the next ten years. However, the traditional ways to manage and communicate with these systems has not changed. In other words, all the information from these systems is not accessible or is not able to be correlated in a way that helps people or businesses do their jobs better and more efficiently, find information they are looking for in the proper context, or make this data consumable in a meaningful way.
There are a variety of specific solutions to handle the rising amount of data found in industry today. These solutions can be categorized into the following types of systems: Enterprise Resource Planning (ERP) systems; Portals and related technology systems; Traditional Business Intelligence systems; and Manufacturing Intelligence systems.
Enterprise Resource Planning systems are used by large and small companies to run their businesses. The typical minimal requirements for these systems are to provide financial and accounting services. However, these systems often have additional functionality for specific vertical industries, such as manufacturing, utilities, construction, and retail by way of example. These Enterprise Resource Planning systems are rigid, in both business process support and data models. They also are very expensive to implement and maintain. Further, these systems are usually implemented to enforce repeatable, standard business processes and it generally is not possible to use these systems for dynamic analysis of different types of data.
Traditional Business Intelligence systems usually rely on specific, detailed data models, such as data warehouses. While the data is typically current, for example about a day old, in these systems, the models are rigid and report writing may require Information Technology (IT) skills. While these systems have become much better at providing users with the ability to self-serve, the self service capability is restricted to the previously designed semantic search models. As a result, these Traditional Business Intelligence systems do not address current conditions, rapidly changing data, third party collaboration, or external data sources.
Manufacturing Intelligence systems (also referred to as Enterprise Manufacturing Intelligence or EMI) are typically concerned with real-time data collected from machines and devices. This time series data usually does not have any business context associated with it. The users of these Manufacturing Intelligence systems typically are plant operators and engineers. These systems do not handle other business related data, do not understand or correlate unstructured data, and are not easily readable.
Currently, most of the utilized solutions to pull all these separate systems with their different sources of data together so users can consume data from more than one of these solutions in a meaningful way, is to execute a complex, multi-year integration project that results in a data mart. Typically, this involves replicating large quantities of data from multiple systems into a rigid model, similar to a hub and spoke model. The hub is the data mart holding all the replicated data. As the systems changes at the end of the spokes, new time consuming integration and modeling is required. Unfortunately, this type of solution is expensive to maintain, the data model and semantics are not dynamic, and the ability to consume data is available only through pre-defined reports.
Other existing approaches to pull all these separate systems with their different sources of data together rely on relational data bases which are adept at answering known questions against known data structures (Known-Known) and can answer known questions against unknown data structures (Known-Unknown). Unfortunately, these existing approaches can not effectively answer unknown questions against known data structure (Unknown-Known), and unknown questions against unknown data structures (Unknown-Unknown).
As a result, currently users of existing technologies to identify and access data are concerned with the timeliness and relevance of acquired data. In particular, there is a concern about deficiencies with accurately identifying and accessing real-time data from devices and other storage systems. Additionally, these existing technologies have difficulties identifying and accessing different types of relevant data, such as business related data which can be stored in many varying formats and unstructured data. Further, these existing technologies typically require large quantities of data from multiple systems to first be entered into a rigid model and then this entered data can only be access in limited manners.